class: center, middle, inverse, title-slide .title[ # Survey Data Analysis with Kobocruncher ] .subtitle[ ## Session 8 - Weighting ] .author[ ### Edouard Legoupil ] .institute[ ### Snr Data Analysis Officer ] .date[ ### 25 November 2022 ] --- ## Review of sampling designs These features can be combined to form one design * __Simple random sampling__: every unit has the same chance of being selected * Without replacement: units can only be selected once * With replacement: units can be selected more than once * __Systematic sampling__: sample n individuals from a ordered list and sampling individuals at an interval with a random starting point * __Probability proportional to size__: probability of selection is proportional to "size" * __Stratified sampling__: divide population into mutually exclusive subgroups (strata). Randomly sample within each stratum * __Clustered sampling__: divide population into mutually exclusive subgroups (clusters). Randomly sample clusters and then individuals within clusters ## Determining the design Look at documentation associated with the analysis file Keywords to look for: methodology, design, analysis guide, technical documentation Documentation will indicate the variables needed to specify the design. Look for: * weight (almost always) * strata and/or clusters/PSUs. Sometimes pseudo-strata and pseudo-cluster OR * replicate weights (this is used instead of strata/clusters for analysis) * might also see finite population correction or population sizes ## Syntax for common designs ```r # simple random sample (SRS) apisrs %>% as_survey_design(fpc = fpc) # stratified sample apistrat %>% as_survey_design(strata = stype, weights = pw) # one-stage cluster sample apiclus1 %>% as_survey_design(ids = dnum, weights = pw, fpc = fpc) # two-stage cluster sample, weights computed from pop size apiclus2 %>% as_survey_design(ids = c(dnum, snum), fpc = c(fpc1, fpc2)) # stratified, cluster design apistrat %>% as_survey_design(ids = dnum, strata = stype, weights =pw, nest = TRUE) ``` --- class: inverse, center, middle # Thank you __Next session__: [09-Publishing](9-Publishing.html) The final stage of the process implies to facilitate both the interpretation of the data and the creation of data story telling content